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Record W2766771602 · doi:10.1103/physrevb.96.155209

Alloying strategy for two-dimensional GaN optical emitters

2017· article· en· W2766771602 on OpenAlexafffund
C. Pashartis, Oleg Rubel

Bibliographic record

VenuePhysical review. B./Physical review. B · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsBand gapMaterials scienceSemiconductorValence bandImpurityOptoelectronicsWide-bandgap semiconductorDirect and indirect band gapsElectronic band structureCondensed matter physicsChemistryPhysics

Abstract

fetched live from OpenAlex

The recent progress in formation of two-dimensional (2D) GaN by a migration-enhanced encapsulated technique opens up new possibilities for group III-V 2D semiconductors with a band gap within the visible energy spectrum. Using first-principles calculations we explored alloying of 2D-GaN to achieve an optically active material with a tunable band gap. The effect of isoelectronic III-V substitutional elements on the band gaps, band offsets, and spatial electron localization is studied. In addition to optoelectronic properties, the formability of alloys is evaluated using impurity formation energies. A dilute highly mismatched solid solution 2D-${\mathrm{GaN}}_{1\ensuremath{-}x}{\mathrm{P}}_{x}$ features an efficient band gap reduction in combination with a moderate energy penalty associated with incorporation of phosphorous in 2D-GaN, which is substantially lower than in the case of the bulk GaN. The group-V alloying elements also introduce significant disorder and localization at the valence band edge that facilitates direct band gap optical transitions, thus implying the feasibility of using III-V alloys of 2D-GaN in light-emitting devices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.400
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2017
Admission routes2
Has abstractyes

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